Optical Character Recognition (OCR) makes a constant appearance in technologies aimed at automating manual processes. It converts printed text to a readable digital format and helps us retrieve every scanned image instantly.
In ATMs, it reads the text of checks and tells the machine the amount it must deposit, regardless of how terrible the handwriting is. Overall, OCR increases access to information, improves customer service, reduces the likelihood of misplaced files.
Unsurprisingly, many companies use OCR as a key technology in document scanning and management solutions. Used in the indexing phase, OCR makes digital images both editable and searchable. That said, document management has now changed and evolved into something more advanced – Cognitive Document Automation.
Cognitive document automation is an intelligent solution that automates the extraction, understanding, and integration of documents needed in organizational processes. While OCR is an essential part of document management, it plays a smaller role in cognitive document automation.
In this article, we will discuss why cognitive document automation isn’t just about OCR.
Why OCR Isn’t Enough
Extracting data from documents is important. However, to automate document management effectively, we need a solution that categorizes the extracted data and transfers it into the correct fields. Most documents have several fields that store valuable information. OCR alone cannot detect the differences between these fields and saved information correctly.
At the same time, document management solutions must determine the type of document precisely. Your company might want to save files in a different format, owing to compliance issues of your industry. This is why detecting the file type accurately is too complex for solutions that entirely rely on OCR.
Businesses need a system that can reliably transfer data from digital and tangible to their document management system without any oversight or manual intervention. If you only use OCR in your digital processes, your document management will remain ineffective.
8 Features Required for Cognitive Document Automation
1-Distributed Capture
Cognitive document management solutions must provide support for field and branch offices with data capture tools, as well as your centralized back-office with production scanners. To reduce the total cost of ownership, a CDA solution must provide central administration, reporting, licensing, as well as scanner profile management.
2-Multi-Channel Capture
CDA solutions must deliver comprehensive multi-channel capture to cater to customer preferences. This includes fax, email, mobile, desktop scanner, folder, web, as well as MFP front-panel integration.
They must create the mobile SDK in a way that allows developers to integrate a wide range of mobile capture features such as image capture, classification, recognition, as well as data extraction and validation. The more features you can provide on specific devices instead of servers, the better experience you can provide to your customers.
3-Mailroom Capture for Multiple Departments
A digital mailroom capture increases transparency and significantly improves access to information for your team, enabling you to save enormous amounts of space, time, and money.
Digital mailroom capture enables organizations to digitize incoming paper documents from emails and create a document workflow suited for modern times. Your CDA solution should convert all incoming content into a digital format and save it in a safe and secure location.
At the same time, it should provide a central repository for both electronic and paper mail formats (fax and email) and incorporate them into the same document workflow. CDA should help you avoid wasted costs and effort in developing and maintaining disparate solutions from different vendors.
It’s important to deliver support for acquisition and understanding important document types such as forms, invoices, onboarding documents, medical documents, shipping documents, mortgage documents, emails, letters, contracts, etc. Thus, it can help deliver a fully comprehensive digital mailroom.
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4-Document Classification
CDA solutions implement smart document classification. Document classification can help companies make sure that the new content aligns with existing business processes, making it easier to secure documents and automate processes. The technology uses multiple methods to tag standard document types and other content related to business processes.
Since CDA solutions use advanced classification to organize documents automatically, companies can classify incoming content as they receive it. In other words, companies can ensure incoming content is marked based on all relevant compliance and security tags.
As a result, it’s easier to implement compliance of geo-location, contracts, as well as, Personally-Identifying Information (PII). At the same time, CDA solutions can also attribute content to an audit trail, which can help in compliance with government regulations.
5-Multiple Languages
Machine learning, managed services, and OCR document processing can provide you with a dynamic and touchless document management system. However, as companies grow, they need to accommodate suppliers and payees from different regions and countries. This is why many intelligent document processing solutions can process data from a variety of languages, helping you take your business to the international stage.
6-Cloud Access
Similarly, cloud access is also a useful feature in automated document processing. Cloud-based document automated solution lets businesses run business operations from anywhere and add multiple users with ease.
Instead of being restricted to an on-premise solution, cloud access lets you make intelligent data management features easily accessible from anywhere, anytime. Therefore, remote workers can easily use these features to keep your business afloat during social restrictions.
7-Machine Learning
Powerful CDA solutions use advanced AI algorithms for natural language processing (NLP), classification, and unsupervised learning (clustering). Machine learning algorithms like clustering and classification work hand-in-hand to classify and group documents with similar properties.
At the same time, some algorithms also refine data extraction and validation, making data more meaningful. Algorithms like NLP help CDA understand the semantic meaning behind extracted data, as it enables the software to understand human language.
Machine learning is a key feature of any Cognitive Document Automation solution. Mostly because it allows the solution to train from sample data and enhance document classification and data extraction intelligence.
8-Document and Data Exports and Integration
Unless your CDA solution has support for the export and integration of documents and data commonly used with ERP and ECM systems, your company can get stuck writing and maintaining integration code.
Before you choose a particular solution, make sure that it has pre-built export connectors. These connectors must be linked to common destination systems and can integrate with unsupported systems, and even systems that don’t have exposed APIs. Here robotic process automation can help you.
As companies keep looking for intelligent solutions to optimize their workflow, cognitive document automation will become the standard for efficient document management across the world.
